Pharmacometabolomics, also known as pharmacometabonomics, is a field which stems from metabolomics, the quantification and analysis of metabolites produced by the body. It refers to the direct measurement of metabolites in an individual's bodily fluids, in order to predict or evaluate the metabolism of pharmaceutical compounds, and to better understand the pharmacokinetic profile of a drug. Alternatively, pharmacometabolomics can be applied to measure metabolite levels following the administration of a pharmaceutical compound, in order to monitor the effects of the compound on certain metabolic pathways(pharmacodynamics). This provides detailed mapping of drug effects on metabolism and the pathways that are implicated in mechanism of variation of response to treatment. In addition, the metabolic profile of an individual at baseline (metabotype) provides information about how individuals respond to treatment and highlights heterogeneity within a disease state. All three approaches require the quantification of metabolites found in bodily fluids and tissue, such as blood or urine, and can be used in the assessment of pharmaceutical treatment options for numerous disease states.
Goals of Pharmacometabolomics Pharmacometabolomics is thought to provide information that complements that gained from other omics, namely genomics, transcriptomics, and proteomics. Looking at the characteristics of an individual down through these different levels of detail, there is an increasingly more accurate prediction of a person's ability to respond to a pharmaceutical compound. The genome, made up of 25 000 genes, can indicate possible errors in drug metabolism; the transcriptome, made up of 85,000 transcripts, can provide information about which genes important in metabolism are being actively transcribed; and the proteome, >10,000,000 members, depicts which proteins are active in the body to carry out these functions. Pharmacometabolomics complements the omics with direct measurement of the products of all of these reactions, but with perhaps a relatively smaller number of members: that was initially projected to be approximately 2200 metabolites, but could be a larger number when gut derived metabolites and xenobiotics are added to the list. Overall, the goal of pharmacometabolomics is to more closely predict or assess the response of an individual to a pharmaceutical compound, permitting continued treatment with the right drug or dosage depending on the variations in their metabolism and ability to respond to treatment.
Pharmacometabolomic analyses, through the use of a metabolomics approach, can provide a comprehensive and detailed metabolic profile or "metabolic fingerprint" for an individual patient. Such metabolic profiles can provide a complete overview of individual metabolite or pathway alterations, providing a more realistic depiction of disease phenotypes. This approach can then be applied to the prediction of response to a pharmaceutical compound by patients with a particular metabolic profile. Pharmacometabolomic analyses of drug response are often coupled or followed up with pharmacogenetics studies. Pharmacogenetics focuses on the identification of genetic variations (e.g. single-nucleotide polymorphisms) within patients that may contribute to altered drug responses and overall outcome of a certain treatment. The results of pharmacometabolomics analyses can act to "inform" or "direct" pharmacogenetic analyses by correlating aberrant metabolite concentrations or metabolic pathways to potential alterations at the genetic level. This concept has been established with two seminal publications from studies of antidepressants serotonin reuptake inhibitors where metabolic signatures were able to define pathway implicated in response to the antidepressant and that lead to identification of genetic variants within a key gene within highlighted pathway as being implicated in variation in response. These genetic variants were not identified through genetic analysis alone and hence illustrated how metabolomics can guide and inform genetic data.
History Although the applications of pharmacometabolomics to personalized medicine are largely only being realized now, the study of an individual's metabolism has been used to treat disease since the Middle Ages. Early physicians employed a primitive form of metabolomic analysis by smelling, tasting and looking at urine to diagnose disease. Obviously the measurement techniques needed to look at specific metabolites were unavailable at that time, but such technologies have evolved dramatically over the last decade to develop precise, high-throughput devices, as well as the accompanying data analysis software to analyze output. Currently, sample purification processes, such as liquid or gas chromatography, are coupled with either mass spectrometry (MS)-based or nuclear magnetic resonance (NMR)-based analytical methods to characterize the metabolite profiles of individual patients. Continually advancing informatics tools allow for the identification, quantification and classification of metabolites to determine which pathways may influence certain pharmaceutical interventions. One of the earliest studies discussing the principle and applications of pharmacometabolomics was conducted in an animal model to look at the metabolism of paracetamol and liver damage. NMR spectroscopy was used to analyze the urinary metabolic profiles of rats pre- and post-treatment with paracetamol. The analysis revealed a certain metabolic profile associated with increased liver damage following paracetamol treatment. At this point, it was eagerly anticipated that such pharmacometabolomics approaches could be applied to personalized human medicine. Since this publication in 2006, the Pharmacometabolomics Research Network led by Duke University researchers and that included partnerships between centers of excellence in metabolomics, pharmacogenomics and informatics (over sixteen academic centers funded by NIGMS) has been able to illustrate for the first time the power of the pharmacometabolomics approach in informing about treatment outcomes in large clinical studies and with use of drugs that include antidepressants, statins, antihypertensives, antiplatelet therapies and antipsychotics. Totally new concepts emerged from these studies on use of pharmacometabolomics as a tool that can bring a paradigm shift in the field of pharmacology. It illustrated how pharmacometabolomics can enable a Quantitative and Systems Pharmacology approach. Pharmacometabolomics has been applied for the treatment of numerous human diseases, such as schizophrenia, diabetes, neural disease, depression and cancer.
Personalized Medicine As metabolite analyses are being conducted at the individual patient level, pharmacometabolomics may be considered a form of personalized medicine. This field is currently being employed in a predictive manner to determine the potential responses of therapeutic compounds in individual patients, allowing for more customized treatment regimens. It is anticipated that such pharmacometabolomics approaches will lead to the improved ability to predict an individual's response to a compound, the efficacy and metabolism of it as well as adverse or off-target effects that may take place in the body. The metabolism of certain drugs varies from patient to patient as the copy number of the genes which code for common drug metabolizing enzymes varies within the population, and leads to differences in the ability of an individual to metabolize different compounds. Other important personal factors contributing to an individual's metabolic profile, such as patient nutritional status, commensal bacteria, age, and pre-existing medical conditions, are also reflected in metabolite assessment., Overall, pharmacometabolomic analyses combined with such approaches as pharmacogenetics, can function to identify the metabolic processes and particular genetic alterations that may compromise the anticipated efficacy of a drug in a particular patient. The results of such analyses can then allow modification of treatment regimens for an optimal outcome.
Current Applications
Predicting treatment outcome
Metabotype informs about treatment outcomes Pharmacometabolomics may be used in a predictive manner to determine the correct course of action in regards to a patient about to undergo some type of drug treatment. This involves determining the metabolic profile of a patient prior to treatment, and correlating metabolic signatures with the outcome of a pharmaceutical treatment course. Analysis of a patient's metabolic profile can reveal factors that may contribute to altered drug metabolism, allowing for predictions of the overall efficacy of a proposed treatment, as well as potential drug toxicity risks that may differ from the general population. This approach has been used to identify novel or previously characterized metabolic biomarkers in patients, which can be used to predict the expected outcome of that patient following treatment with a pharmaceutical compound. One example of the clinical application of pharmacometabolomics are studies that looked to identify a predictive metabolic marker for the treatment of major depressive disorder (MDD)., In a study with antidepressant Sertraline, the Pharmacometabolomics Network illustrated that metabolic profile at baseline of patients with major depression can inform about treatment outcomes. In addition the study illustrated the power of metabolomics for defining response to placebo and compared response to placebo to response to sertraline and showed that several pathways were common to both. In another study with escitalopram citalopram, metabolomic analysis of plasma from patients with MDD revealed that variations in glycine metabolism were negatively associated with patient outcome upon treatment with selective serotonin reuptake inhibitors (SSRIs), an important drug class involved in the treatment of this disease.
Monitoring drug-related alterations in metabolic pathways